Comments (6)
looking forward to this being released! looks like the code was just merged. fingers-crossed.
from aws-step-functions-data-science-sdk-python.
Hi Chris,
We are currently investigating the options to integrate with the new SageMaker Processing Jobs. The AWS Step Functions (the service) needs to implement this new service integration first, then we can integrate it into the Python SDK.
Shunjia
from aws-step-functions-data-science-sdk-python.
Please keep us updated on this!! I would love for this feature to exist. I use processing jobs in my ML workflow and would love to be able to use a step function to control my pipeline instead of using lambda functions that rely on s3 triggers based on a put operation. Thank you!
from aws-step-functions-data-science-sdk-python.
@shunjd we have a use-case for this. Could you please let us know what would be a timeline for this integration?
from aws-step-functions-data-science-sdk-python.
@shunjd I am also interested in the processing step. Is there a way to submit SageMaker processing job using LambdaStep? I assume there should be created a lambda function that submits SageMaker processing jobs.
from aws-step-functions-data-science-sdk-python.
Hi @cfregly Please refer to my new notebook demonstrating the new ProcessingStep here -
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/step-functions-data-science-sdk/step_functions_mlworkflow_processing/step_functions_mlworkflow_scikit_learn_data_processing_and_model_evaluation.ipynb
from aws-step-functions-data-science-sdk-python.
Related Issues (20)
- Is it Possible to use schema from ExecutionInput into container_arguments of ProcessingStep? HOT 6
- Feature Request: Add Workflow.create_or_update() to update workflow if it exists or create it if it doesn't HOT 1
- feature: adding support for AsyncInferenceConfig in endpoint config HOT 1
- Unable set ModelClientConfig in TransformerStep HOT 3
- object of type TrainingInput is not JSON serializable HOT 1
- Make sagemaker an optional dependency HOT 5
- [aws-stepfunctions] Support for ResultSelector HOT 1
- Allow drop-in states language HOT 1
- Support "Cycles" with Choice states HOT 1
- pip install stepfunctions fails in SageMaker Studio Notebook
- render_graph() has hidden dependencies
- Workflow Tags are not updated HOT 1
- Retry policies with error codes HOT 1
- stepfunctions package side effect of changing application's root logger logging level to ERROR
- Instructions in CONTRIBUTING no longer work with tox>=4
- Project Maintenance HOT 5
- Execution inputs as container arguments for processing jobs HOT 4
- Stepfunctions-Textract-StartDocumentTextDetection does not accept SNSTopicArn in NotificationChannel HOT 1
- Please add Distributed Map
- adding tags to a Sagemaker estimator in the training step does not seem to be supported
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from aws-step-functions-data-science-sdk-python.